Service Delivery Center, AI & Data, AI Developer - Senior

Location: San Antonio and Dallas

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We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.

The O pportunity

Supports the delivery of solution or infrastructure development services for AI/ML initiatives, applying strong technical capability and hands-on engineering experience. Contributes to the design, development, delivery, and maintenance of AI-enabled solutions or infrastructure while aligning to relevant engineering standards and project delivery expectations. Understands user requirements and helps translate them into sound technical designs and implementation plans. Contributes to the integration of AI/ML capabilities into broader enterprise solutions, with a focus on quality, scalability, and user impact.

Your key responsibilities

• Develop, test, deploy, and support production-grade AI/ML, generative AI, and intelligent automation solutions. • Solve complex technical problems across development, integration and production support through coding, debugging, testing, troubleshooting, and structured design remediation. • Translate user requirements into technical designs, APIs, workflows, and supportable implementation patterns. • Build and integrate LLM, RAG, and agentic solution components into enterprise solutions, applications and platforms. • Support project delivery through disciplined execution, estimation, documentation, status’ communication, and risk identification. • Participate in design reviews, providing thoughtful trade-off analysis and implementation input. • Use modern AI-assisted software engineering tools such as Claude Code, Codex, or equivalent agentic coding platforms as part of day-to-day engineering delivery to improve delivery speed, code quality and engineering efficiency.

AI and Engineering Skills:

Gen AI Foundational:

• Experience designing, building, and maintaining production-grade LLM applications, including end-to-end pipelines from data ingestion through model output delivery (e.g. Azure OpenAI, AWS Bedrock, Google Vertex AI etc.). • Demonstrated practical experience building retrieval-augmented systems that ground model outputs in enterprise knowledge sources, including chunking strategies, embedding pipelines, and retrieval optimization (e.g. LlamaIndex, LangChain, Pinecone, Weaviate, Azure AI Search, pgvector etc.). • Working technical knowledge of embedding models, vector search, and semantic retrieval patterns used to ground LLM outputs in enterprise knowledge sources (e.g. OpenAI Embeddings, Azure AI Search, pgvector etc.). • Proficiency in prompt engineering techniques including zero-shot, few-shot, chain-of-thought, and structured output design, with the ability to systematically evaluate and iterate on prompt performance (e.g. DSPy, PromptFlow etc.).